Sclerotinia rot caused by the fungus Sclerotinia sclerotiorum (Lib.) de Bary has become a major issue in India’s key rapeseed-mustard growing regions, with a disease incidence of 70–80% in states like Rajasthan, Punjab and Haryana. Once the pathogen is established in a field, it is challenging to control, so it is crucial to understand its dispersal mechanism with a view to achieve effective disease management. Taking the aforementioned into account, a comprehensive study was undertaken, in order to elucidate how the epidemiological factors contribute in the spread of Sclerotinia rot and to develop effective preventive strategies for its management. The present study specifically examines the impact of varying sowing dates and weather parameters on petal infestation (mediated by fungal ascospores) and the incidence of Sclerotinia stem rot in two rapeseed-mustard varieties, Varuna and Kranti, over two consecutive growing seasons (2021–22 and 2022–23). The results showed that the plant sown on Oct. 29 had highest petal infestation (21.8%), however the lowest petal infestation (12.4%) was recorded on Oct. 01 sown crop. Across all-sowing dates, the highest petal infestation (22.5–28.8%) was observed on 3rd Standard Meteorological Week (Jan.15–21.) which revealed that a prevailing maximum and minimum temperature (16.4 andand 7.6 °C) coupled with maximum and minimum Relative Humidity (90 andand 65%) coincided with weekly rainfall (1.4 mm) and sunshine (2.4 h.) favoured petal infestation. The petal infestation was negatively and strongly correlated with maximum temperature and sunshine with r value of − 0.919 to − 0.928 and − 0.834 to − 0.844 respectively. In contrast, it was positively and strongly correlated with minimum Relative Humidity and rainfall (r: 0.841–0.853 and 0.586–0.621) which promoted spread of the disease. Similarly, the maximum Sclerotinia stem rot incidence (23.4–23.7%) was found on Oct. 29 sown crop which indicates that the mean weather conditions viz. maximum and minimum temperature (19.2 andand 8.5 °C), maximum and minimum Relative Humidity (92 andand 61%), total rainfall (81.4 mm) and sunshine (4.2 h.) during the disease period was found more favorable. Disease incidence was found negatively and strongly correlated with maximum temperature and sunshine (r: −0.576 to − 0.644 and − 0.570 to − 0.642) but positively and strongly correlated with rainfall and minimum Relative Humidity (r: 0.829–0.835 and 0.626 − 0.676). Disease incidence was also positively and strongly correlated with petal infestation (r: 0.891–0.926). By using the weather data, the author developed stepwise linear regression equations which can predict petal infestation and disease incidence with an accuracy of 84.5–92.9% and 68.7–69.7% respectively. Moreover, the regression equations based on petal infestation could predict Sclerotinia stem rot disease with 79.5–85.7% accuracy. Among the different growth models tested, the Logistic model performed best with higher r2 value (0.520–0.546), making it a useful tool for predicting the progression of petal infestation over time.

Utilizing petal infestation and predictive models to forecast Sclerotinia stem rot of rapeseed-mustard

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2025年7月7日

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Marjit Chandam,
A. K. Tewari,
Rahul Purohit,
Ruchi Tripathi

摘要

Sclerotinia rot caused by the fungus Sclerotinia sclerotiorum (Lib.) de Bary has become a major issue in India’s key rapeseed-mustard growing regions, with a disease incidence of 70–80% in states like Rajasthan, Punjab and Haryana. Once the pathogen is established in a field, it is challenging to control, so it is crucial to understand its dispersal mechanism with a view to achieve effective disease management. Taking the aforementioned into account, a comprehensive study was undertaken, in order to elucidate how the epidemiological factors contribute in the spread of Sclerotinia rot and to develop effective preventive strategies for its management. The present study specifically examines the impact of varying sowing dates and weather parameters on petal infestation (mediated by fungal ascospores) and the incidence of Sclerotinia stem rot in two rapeseed-mustard varieties, Varuna and Kranti, over two consecutive growing seasons (2021–22 and 2022–23). The results showed that the plant sown on Oct. 29 had highest petal infestation (21.8%), however the lowest petal infestation (12.4%) was recorded on Oct. 01 sown crop. Across all-sowing dates, the highest petal infestation (22.5–28.8%) was observed on 3rd Standard Meteorological Week (Jan.15–21.) which revealed that a prevailing maximum and minimum temperature (16.4 andand 7.6 °C) coupled with maximum and minimum Relative Humidity (90 andand 65%) coincided with weekly rainfall (1.4 mm) and sunshine (2.4 h.) favoured petal infestation. The petal infestation was negatively and strongly correlated with maximum temperature and sunshine with r value of − 0.919 to − 0.928 and − 0.834 to − 0.844 respectively. In contrast, it was positively and strongly correlated with minimum Relative Humidity and rainfall (r: 0.841–0.853 and 0.586–0.621) which promoted spread of the disease. Similarly, the maximum Sclerotinia stem rot incidence (23.4–23.7%) was found on Oct. 29 sown crop which indicates that the mean weather conditions viz. maximum and minimum temperature (19.2 andand 8.5 °C), maximum and minimum Relative Humidity (92 andand 61%), total rainfall (81.4 mm) and sunshine (4.2 h.) during the disease period was found more favorable. Disease incidence was found negatively and strongly correlated with maximum temperature and sunshine (r: −0.576 to − 0.644 and − 0.570 to − 0.642) but positively and strongly correlated with rainfall and minimum Relative Humidity (r: 0.829–0.835 and 0.626 − 0.676). Disease incidence was also positively and strongly correlated with petal infestation (r: 0.891–0.926). By using the weather data, the author developed stepwise linear regression equations which can predict petal infestation and disease incidence with an accuracy of 84.5–92.9% and 68.7–69.7% respectively. Moreover, the regression equations based on petal infestation could predict Sclerotinia stem rot disease with 79.5–85.7% accuracy. Among the different growth models tested, the Logistic model performed best with higher r² value (0.520–0.546), making it a useful tool for predicting the progression of petal infestation over time.